Lab5x
Narciso
9/23/2020
Data for Lab
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, uniontime_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") ##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## i Use `spec()` for the full column specifications.# Let's get the times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## i Use `spec()` for the full column specifications.# Create Keys
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
###Graphic Output
pdf("Images/time_series_example_plot.pdf", width=6, height=3)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2# Plot graph to a png outputfile
ppi <- 300
png("Images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2
### Interactive Graphs
# Version 2
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
)library(plotly)
# Subset the time series data to include US deaths
US_deaths <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US")
# Collect the layers for agraph of the US time series data for covid deaths
p <- ggplot(data = US_deaths, aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
# Plot the graph using ggplotly
ggplotly(p)library(gganimate)
library(transformr)
theme_set(theme_bw())
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# Some people needed to use this line instead
# animate(p,renderer = gifski_renderer(), end_pause = 15)
animate(p, end_pause = 15)
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# Some people needed to use this line instead
# animate(p,renderer = gifski_renderer(), end_pause = 15)
anim_save("deaths_5_countries.gif", p)###Exercises1
ppi <- 300
png("Images/time_series_Mine_plot.png", width=3*ppi, height=3*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths Dark")+
theme_dark()
dev.off()## png
## 2
US COVID-19 Deaths Dark
###Exercise 2
ggplotly(
time_series_long_joined_counts %>%
group_by(Country_Region, Report_Type, Date) %>%
summarise(Counts = sum(Counts)) %>%
filter (Country_Region == "US") %>%
filter (Report_Type == "Confirmed") %>%
ggplot(aes(x = Date, y = log2(Counts), fill = Report_Type, color = Report_Type)) +
geom_point() +
geom_line() +
ggtitle("Confirmed US COVID-19 Cases") +
facet_wrap(~Country_Region, nrow=5, scales="free") +
theme_dark()
)## `summarise()` regrouping output by 'Country_Region', 'Report_Type' (override with `.groups` argument)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.time_series_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## i Use `spec()` for the full column specifications.time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## i Use `spec()` for the full column specifications.time_series_confirmed_long <- time_series_confirmed %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_deaths <- read_csv("data/time_series_covid19_deaths_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## i Use `spec()` for the full column specifications. time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
head(time_series_confirmed_long)## # A tibble: 6 x 7
## Key Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/22/~ 0
## 2 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/23/~ 0
## 3 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/24/~ 0
## 4 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/25/~ 0
## 5 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/26/~ 0
## 6 NA.Afghanistan.1/2~ <NA> Afghanistan 33.9 67.7 1/27/~ 0time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 0time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
head(time_series_long_joined_counts)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Report_Type Counts
## <chr> <chr> <dbl> <dbl> <date> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 Confirmed 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-22 Deaths 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-23 Confirmed 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-23 Deaths 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-24 Confirmed 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-24 Deaths 0data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("US","Brazil","India","Mexico","UK","Italy","France", "Iran", "Spain", "Russia"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Top 10 Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# Some people needed to use this line instead
# animate(p,renderer = gifski_renderer(), end_pause = 15)
animate(p, end_pause = 15)